Human–AI interaction is a field of research and a sub-field of human–computer interaction, focusing on user experience and psychological factors. With the proliferation of artificial intelligence (AI), there has developed a subsection of HCI research dedicated to artificial intelligence and how people interact with and are impacted by it.
Introduction Artificial intelligence has fluid definitions and varied research applications, but in brief can be applied to mechanizing tasks that would require human intelligence to complete. AI are tools designed to replicate the human abilities of navigating uncertainty, active learning, and processing information in different contexts.
Within the context of HCI and HAX research, artificial intelligence can be broken into two sub-fields, natural language processing (NLP) and computer vision (CV). AI technologies notably include machine-learning, deep-learning and neural networks, and large-language models (LLMs).
As a new and rapidly developing technology, unlike human-computer interaction, where a human directs a machine, human-AI interaction is characterized by a more collaborative relationship between the computer program (the AI) and the human user, as AI is perceived as an active agent rather than a tool. This changing dynamic creates new questions and necessitates new research methods that are not present in traditional HCI research.
According to a scoping review on the state of the discipline, the HAX field comprises research on the "design, development, and evaluation of AI systems" and encompasses the themes of human-AI collaboration, human-AI competition, human-AI conflict, and human-AI symbiosis.
Design Machine learning and artificial intelligence have been used for decades in targeted advertising and to recommend content in social media.
Ethical Guidelines (Framework for ethical AI development)
User experience
AI has been viewed with various expectations, attributions, and often misconceptions. Many people exclusively understand AI as the LLM chatbots they interact with, like ChatGPT or Claude, or other generative AI programs. Human interaction with these chatbots is heavily affected by the Forer effect, leading to undue belief in the chatbot's accuracy and efficacy.
Most fundamentally, humans have a mental model of understanding AI's reasoning and motivation for its decision recommendations, and building a holistic and precise mental model of AI helps people create prompts to receive more valuable responses from AI. However, these mental models are not whole because people can only gain more information about AI through their limited interaction with it.
Research on human-AI interaction has emphasized that users develop mental models of AI systems and revise those models through repeated use, feedback, and explanation, while design research has stressed the importance of communicating capabilities and limitations early and supporting trust calibration through explanation and correction. In a 2025 SSRN working paper, John DeVadoss proposed "Hypothetico-Deductive Interaction" (HDI), a framework that describes human-AI interaction as a mutual process of conjecture and refutation in which users test assumptions about an AI system's capabilities while the system infers and updates assumptions about user goals through its responses and clarifying questions. DeVadoss argued that this framing helps explain prompt iteration, weak capability awareness, and trust miscalibration, and suggested design responses such as clearer communication of uncertainty, easier correction, actionable explanations, and safer failure modes.
Research themes Human-AI collaboration occurs when the human and AI supervise the task on the same level and extent to achieve the same goal. Some collaboration occurs in the form of augmenting human capability. AI may help human ability in analysis and decision-making through providing and weighing a volume of information, and learning to defer to the human decision when it recognizes its unreliability. It is especially beneficial when the human can detect a task that AI can be trusted to make few errors so that there is not a lot of excessive checking process required on the human's end.
Some findings show signs of human-AI augmentation, or human–AI symbiosis, in which AI enhances human ability in a way that co-working on a task with AI produces better outcomes than a human working alone. For example: the quality and speed of customer service tasks increase when a human agent collaborates with AI, training on specific models allows AI to improve diagnoses in clinical settings, and AI with human-intervention can improve creativity of artwork while fully AI-generated haikus were rated negatively. AI can provide learners with AI-based learning support, which benefits learners' knowledge acquisition and their affective experience.
Human-AI synergy, a concept in which human-AI collaboration would produce more optimal outcomes than either human or AI working aloneKamar, E. (2016, July). Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence. In IJCAI (pp. 4070-4073). could explain why AI does not always help with performance. Some AI features and development may accelerate human-AI synergy, while others may stagnate it. For example, when AI updates for better performance, it sometimes worsens the team performance with human and AI by reducing the compatibility with the new model and the mental model a user has developed on the previous version. Research has found that AI often supports human capabilities in the form of human-AI augmentation and not human-AI synergy, potentially because people rely too much on AI and stop thinking on their own. Prompting people to actively engage in analysis and think when to follow AI recommendations reduces their over-reliance, especially for individuals with higher need for cognition.
Human-AI competition
Robots and computers have substituted routine tasks historically completed by humans, but agentic AI has made it possible to also replace cognitive tasks including taking phone calls for appointments and driving a car. At the point of 2016, research has estimated that 45% of paid activities could be replaced by AI by 2030.
Perceived autonomy of robots is known to increase people's negative attitude toward them, and worry about the technology taking over leads people to reject it. There has been a consistent tendency of algorithm aversion in which people prefer human advice over AI advice. However, people are not always able to tell apart tasks completed by AI or other humans. This sentiment is more prominent in the Western cultures as Westerners tend to show less positive views about AI compared to East Asians.
Research on the psychological impacts of AI
As much as people perceive and make judgment about AI itself, they also form impressions of themselves and others who use AI. In the workplace, employees who disclose the use of AI in their tasks are more likely to receive feedback that they are not as hardworking as those who are in the same job who receive non-AI help to complete the same tasks. AI use disclosure diminishes the perceived legitimacy in the employee's task and decision making which ultimately leads observers to distrust people who use AI. Although these negative effects of AI use disclosure are weakened by the observers who use AI frequently themselves, the effect is still not attenuated by the observers' positive attitude towards AI.
A 2026 review of this literature organized the findings around perceived competence and perceived warmth. It reported that people who use generative AI tend to rate themselves as more competent but less warm, while observers rate them lower on both dimensions, an asymmetry attributed to observers lacking access to the user's reasons for turning to the tool. Reported downstream effects include reduced willingness to hire or reward users, and a tendency among users to conceal their use of the technology.
Although AI provides a wide range of information and suggestions to its users, AI itself is not free of biases and stereotypes, and it does not always help people reduce their cognitive errors and biases. People are prone to such errors by failing to see other potential ideas and cases that are not listed by AI responses and committing to a decision suggested by AI that directly contradicts the correct information and directions that they are already aware of. Gender bias is also reflected in the female gendering of some AI chatbots, conceptualizing females as a helpful assistant.
Emotional connection with AI
Human-AI interaction has been theorized in the context of interpersonal relationships mainly in social psychology, communications and media studies, and as a technology interface through the lens of human-computer interaction and computer-mediated communication.
As large language models get trained on ever-larger datasets and with more sophisticated techniques, their ability to produce natural, human-like sentences has improved to the point that language learners can have simulated natural conversations with AI models to improve their fluency in a second language. Companies have developed AI human companion systems specialized in emotional and social services (e.g. Replika, Chai, Character.ai) separate from generative AI designed for general assistance (e.g. ChatGPT, Google Gemini).
Differences between human-human relationships Human-AI relationships are different from human-human friendships in a few distinct ways. Human-human relationships are defined with mutual and reciprocal care, while AI chatbots have no say in leaving a relationship with the user as bots are programmed to always engage. Although this type of power imbalance would be characteristic of an unhealthy relationship in human-human relationships, it is generally accepted by the user as a default of human-AI relationships. Human-AI relationships also tend to be more focused around the user's need over shared experience.
Human-AI friendship AI has increasingly played a part in people's social relationships. Particularly, young adults use AI as a friend and a source of emotional support. The market for AI companion services was 6.93 billion U.S. dollars in 2024 and is expected to reach beyond 31.1 billion U.S. dollars by 2030. For example, Replika, the most known social AI companion service in English has over 10 million users.
People who use AI as a friend show signs of emotional attachment by maintaining frequent contact with a chatbot like keeping the app with the microphone on open during work, using it as a safe haven by sharing their personal worries and concerns, or using it as a secure base to explore friendship with other humans while maintaining communication with an AI chatbot. Some reported having used it to replace a social relationship with another human being. People particularly appreciate that AI chatbots are agreeable and do not judge them when they disclose their thoughts and feelings. Moreover, research has shown that people tend to find it easier to disclose personal concerns to a virtual chatbot than a human. Some users express that they prefer Replika as it is always available and shows interest in what the users have to say which makes them feel safer around an AI chatbot than other people. A recent review of studies comparing AI-generated and human-written emotional support described two opposing effects. Text produced by large language models was generally rated as more empathic than text written by people, including crowdworkers, physicians and trained crisis-line responders, but the same text was rated lower once recipients believed it came from an AI. When given a choice, participants tended to prefer support from a person even after rating the AI-generated text more highly, and even when the human response involved a wait.